tdhook

name	tdhook
description	Provides guidance for interpreting and manipulating neural network internals using tdhook with TensorDict and PyTorch hooks. Use when needing attribution maps, activation analysis, probing, steering, activation patching, or weight-level interventions on PyTorch or TensorDict models.
version	1.0.0
author	Xmaster6y
license	MIT
tags	["tdhook","Interpretability","Attribution","Activation Analysis","Probing","Steering","TensorDict","PyTorch Hooks","GradCAM","LRP","Activation Patching"]
dependencies	["tdhook","tensordict>=0.3.0","torch>=2.0.0"]

tdhook

Interpretability with TensorDict and PyTorch hooks. One pattern: with Method(...).prepare(model) as hooked: td = hooked(td).

Docs: Home · Methods · Tutorials · API · GitHub: Xmaster6y/tdhook · Paper: arXiv:2509.25475

When to Use

Use tdhook when you need to:

• Compute input attributions (Saliency, IntegratedGradients, GradCAM, LRP)
• Capture or patch activations at arbitrary layers
• Train probes (linear, bilinear) on representations
• Steer model behavior (ActivationAddition, SteeringVectors)
• Apply weight-level changes (Pruning, Adapters, TaskVectors)
• Work with TensorDictModule, TorchRL, or HuggingFace models

Consider alternatives: nnsight (remote 70B+), pyvene (declarative configs), TransformerLens (cached activations).

---

Workflow 1: Input Attribution (IntegratedGradients)

Goal: Which inputs (pixels, tokens) matter most for a prediction?

Checklist:

• Define init_attr_targets(td, ctx) returning TensorDict with target outputs
• Create baseline TensorDict (zeros, blurred, or neutral)
• Use TensorDict({"input": x, ("baseline", "input"): baseline})
• Call hooked(td); read td.get(("attr", "input"))
• Visualize: heatmap for images, token-level for text

from tdhook.attribution import IntegratedGradients

def init_attr_targets(targets, _):
    return TensorDict(out=targets["output"][..., class_idx], batch_size=targets.batch_size)

with IntegratedGradients(init_attr_targets=init_attr_targets).prepare(model) as hooked:
    td = hooked(TensorDict({"input": x, ("baseline", "input"): baseline}))
    attr = td.get(("attr", "input"))

---

Workflow 2: Linear Probing on Layer Activations

Goal: Train probes on representations for classification/diagnostics.

Checklist:

• Create ProbeManager(sklearn_model, kwargs, compute_metrics)
• Add labels and step_type ("fit" / "predict") to TensorDict
• Choose layer regex: e.g. "transformer.h.(0|5|10).mlp$"
• Pass additional_keys=["labels", "step_type"] to Probing
• For HuggingFace: in_keys=["input_ids"], out_keys=["logits"]
• Call hooked(train_td) then hooked(test_td); read manager.fit_metrics, manager.predict_metrics

from tdhook.latent.probing import Probing, ProbeManager

manager = ProbeManager(LogisticRegression, {}, compute_metrics)
with Probing(
    "transformer.h.(0|5|10).mlp$",
    manager.probe_factory,
    additional_keys=["labels", "step_type"],
).prepare(model, in_keys=["input_ids"], out_keys=["logits"]) as hooked:
    hooked(train_td)  # step_type="fit"
    hooked(test_td)   # step_type="predict"

---

Workflow 3: Capture or Override Activations (Low-Level)

Goal: Inspect or patch activations at specific modules without high-level methods.

Checklist:

• Use hooked_module.run(data, grad_enabled=...) for low-level control
• Inside context: run.save("path.to.module") to capture
• Use run.set("path.to.module", tensor) to override
• Call proxy.resolve() after the run to read cached tensors
• For gradients: run.save_grad(...), run.set_grad(...)

with hooked_module.run(data, grad_enabled=True) as run:
    run.save("layers.5.mlp")
    run.set("layers.5.attn", override_tensor)
cached = run.get("layers.5.mlp", cache_key="my_key").resolve()

---

Quick Patterns

# Attribution (needs baseline for IG)
with IntegratedGradients(init_attr_targets=init_fn).prepare(model) as hooked:
    td = hooked(TensorDict({"input": x, ("baseline", "input"): baseline}))
    attr = td.get(("attr", "input"))

# Steering: extract and apply
with ActivationAddition(["layer.7.mlp"]).prepare(model) as hooked:
    steer = hooked(TensorDict({("positive","input"): pos, ("negative","input"): neg})).get(("steer","layer.7.mlp"))
with SteeringVectors(modules_to_steer=["layer.7.mlp"], steer_fn=lambda k, o: o + scale*steer).prepare(model) as hooked:
    out = hooked(TensorDict({"input": x}))

Key TensorDict Keys

Key	Purpose
("baseline", "input")	Attribution baseline
("positive", "input"), ("negative", "input")	Steering extraction
("patched", "input")	Patching source
("attr", key)	Attribution output
labels, step_type	Probing (via additional_keys)

---

Common Issues & Troubleshooting

Issue	Solution
KeyError on TensorDict	Use tuple keys: ("attr", "input"), ("baseline", "input")
HuggingFace model fails	Pass in_keys=["input_ids"], out_keys=["logits"] to prepare()
BilinearProbeManager shape mismatch	Call manager.before_all() before forwards, manager.after_all() after
Module path not found	Use relative=False or correct regex; see api.md Module Path Resolution
Probing step_type missing	Add additional_keys=["labels", "step_type"] and pass both in TensorDict
IG baseline wrong shape	Ensure ("baseline", "input") matches "input" shape and device

See references/issues.md for more patterns.

---

Feedback Loops for Quality-Critical Operations

• Attribution: Validate with Infidelity/Sensitivity metrics (tdhook.metrics) before trusting heatmaps
• Probing: Compare train vs test metrics to detect overfitting; use cross-validation for probe selection
• Steering: Ablation: remove steer at different layers to verify effect

---

Method Selection Guide

Need	Primary Class	Key Params
Gradient w.r.t. input	Saliency	init_attr_targets, input_modules
Path-integral attribution	IntegratedGradients	init_attr_targets, n_steps, baseline in TensorDict
Channel-weighted spatial	GradCAM	modules_to_attribute (path → DimsConfig)
Extract contrast vector	ActivationAddition	module list, ("positive","input"), ("negative","input")
Apply precomputed vector	SteeringVectors	steer_fn(module_key, output)
Replace activations	ActivationPatching	patch_fn, ("patched","input")
Train classifiers on reps	Probing	key_pattern, probe_factory, additional_keys
Zero params by importance	Pruning	importance_callback, amount_to_prune
Insert modules inline	Adapters	adapters={path: (adapter, source, target)}

---

Setup & Installation

pip install tdhook tensordict torch

For optional captum-based attribution or sklearn probing:

pip install captum scikit-learn

Colab dev setup: see tutorials.md Setup section.

---

Pitfalls to Avoid

• IG without baseline: IntegratedGradients requires ("baseline", "input"); use zeros or neutral input
• Probing without step_type: Probing needs step_type in TensorDict ("fit" for training, "predict" for eval)
• Nested keys as strings: Use ("attr", "input") not "attr/input" for TensorDict
• Wrong module path: Use regex that matches actual submodule names; transformer.h.5.mlp vs layers.5.mlp depends on model

---

References

File	Contents
references/README.md	Overview
references/api.md	Full API: HookedModule, methods by category
references/tutorials.md	Use-case tutorials
references/issues.md	GitHub issues & solutions
references/releases.md	Version history
references/file_structure.md	Codebase navigation

Official docs: Home · Methods · Tutorials · API Reference